Consistent Data Quality refers to the state of a data resource where the quality of existing data is thoroughly understood and the desired quality of the data resource is known. It is a state where disparate data quality is known, and the existing data quality is being adjusted to the level desired to meet the current and future business information demand.
Data is the most important component of a computer system. A common concept in computer science is called Garbage in Garbage Out (GIGO) which refers to the fact that no matter how sophisticated and perfect any software application or computer systems is, if the data entered is not the correct data or not of good quality, the output will always be garbage. In programming, poor data quality may cause a bug which is hard to trace.
Data are said to be of high quality, according to JM Juran, "if they are fit for their intended uses in operations, decision making and planning". In business intelligence, data are of high quality if they accurately represent the real life construct that they represent.
Data warehouses are the main repositories of company business data which include all current and history data. Business intelligence mainly relies on these data warehouses so they can know the industry trends. With the information recommended by the business intelligence, a company can already strategize to gain competitive edge over competitors. For instance, if they know that the products or services of the competing company is gaining strong acceptance among the customer and the effect of this is reflected in the analysis of the company's business intelligence, the decision makes can try to come up with innovations to cope up with the competitor.
And so companies should make a strong emphasis on having consistent data quality so they do not get garbage information from the data warehouse. Marketing efforts typically focus on name, address and client buying habits information but data quality is important in all other aspects as well. The principle behind quality data encompasses other important aspects of enterprise management like supply chain data and transactional data.
The difficult part with dealing with data is that it may sometimes be very difficult or to an extreme case, impossible to tell which is good quality data and which is bad quality data. Both could be reported as identical through the same application interface. But there are some guides to improve and have consistent quality data within the business organization.